Problem
Research questions and friction points this paper is trying to address.
indoor air quality
health problems
passive monitoring systems
Large Language Model based air quality monitoring and localized alert generation
🤖 AI Summary
为解决室内空气质量差导致的健康问题，使用大型语言模型和物联网技术监测空气质量并生成位置特定警报。
📝 Abstract
Poor indoor air quality can cause up to five times more direct health problems to occupants than outdoor air. In particular, it may cause headaches, fatigue, eye/throat irritation, and long-time exposure is linked to respiratory and heart as well as some forms of cancer. Despite the importance of indoor health and well-being, most current monitoring devices and systems (usually for offices and workspaces) are passive. The Environmental Quality Monitor (EnQyMo) platform is a generic Internet of Things (IoT) middleware designed to process several sensor data related to air quality in indoor spaces and correlate this data with health exposure risks of users/workplace employees. Using Bluetooth Low Energy (BLE) beacons and a mobile IoT middleware it is able to identify the (smartphone) users exposed to these polluted air or high CO2 (carbon dioxide) levels, and generate location-specific alarms only to the users at the places with the unhealthy air conditions. At the core of EnQyMo is an agency of Large Language Models (LLMs) capable of interpreting regulatory standards and scientific literature to automatically identify critical health exposure levels.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Indoor Air Quality Monitoring
Localized Alerts
Bluetooth Low Energy Beacons
IoT Middleware
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